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Molecular Dynamics Visualization

Introduction

Particle based visualization from a molecular dynamics simulation consisting of 1.28 million CO2 molecules.

Large datasets of particle data still pose a big challenge for visual analyzing methods. Explorative analysis is important when unexpected effects inside the data occur. For this kind of analysis, interactive visualization provides an optimal method. Subproject D.3 of SFB 716 faces the challenge of visualizing large, time-dependent particle datasets and develops new methods and algorithms to reach this goal. Major enhancements to particle visualization are needed as well as close cooperation with domain experts inside the SFB to accomplish this task.

Particle-based visualization has been developed further massively in SFB subprojects. The point-based visualization approach as well as GPU-raycasting of implicit surfaces makes is possible to interactively visualize datasets containing millions of particles at high quality. The large size, and the time-dependency of these datasets, the data transfer between secondary storage, main memory and graphics memory becomes a severe bottleneck and transfer methods have been studied and optimized. A two-phase culling approach has been developed and optimized for particle-based data which allows for interactive representation of 107 to 109 particles on a standard workstation.

To improve representation quality besides rendering speed as well the “deferred shading” based visualization has been extended. The aspect of optimal rendering methods for complex glyph, like dipole glyphs, composite glyphs for molecules without inner degrees of freedom, and polyhedron-based glyphs for porous media, representations has been investigated as well. To enhance perception and understanding of the presented data, filtering is needed, like visualization of crystal defects in metals. Structures inside datasets are of special interest if we want to observe temporal development of this data. To compensate for overlap and visual overload when using path lines the dataset has been filtered using new, density conserving clustering techniques. Based on this work other static representations of dynamic phenomena will be developed for other subprojects inside SFB, allowing more effective analysis.

MegaMol™

To this end, an extensible and adoptable visualization framework has been developed during the first funding period of the SFB 716: MegaMol™. Highly optimized renderers and data structures form the basis for the current visualization research for this subproject, as well as for the visualization subprojects D.4 and D.5 of the SFB. The modular architecture of the software, adaptable at run time, and the extensibility through a plug-in interface and simple programming interfaces MegaMol™ is optimally suited for the wide range of different demands of the subprojects of the SFB 716.

MegaMol™ succeeds MolCloud, which has been developed at the University of Stuttgart in order to visualize point-based datasets. MegaMol™ is written in C++, and uses an OpenGL as Rendering-API and GLSL-Shader. It supports the operating systems Microsoft Windows (Vista, 7) and Linux (Suse), each in 32-bit and 64-bit versions. In large parts, MegaMol™ is based on VISlib (website currently only available in german), a C++-class library for scientific visualization, which has also been developed at the University of Stuttgart.

MegaMol™MegaMol™ is a visualization middleware used to visualize point-based molecular datasets.

DLR Landesstiftungsprojekt 688: Visualisierung der Keimbildung in Mischungen für skalenübergreifende Modelle (completed)This Project formed the foundations for the Projects D.3 and D.4 established in the SFB 716 by providing essential work in the field of interactive visualization of molecular dynamics simulation data. The funding of this project by the Landesstiftung Baden-Württemberg ended at the end of 2006.

MolCloud (completed)MolCloud is a visualization utility for molecular dynamic data sets, developet at the university of stuttgart. In fall 2006 the project was ended and the work on MegaMol™ has begun as follow-up project.

PointCloud (completed)PointCloud accelerates rendering of scattered point data by a hierarchical data structure based on a PCA clustering procedure and thus formed the starting project of point-based visualization at our institute.